Papers with Code 아카이브
3D Face Reconstruction
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3D Face Reconstruction is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.
<span style="color:grey; opacity: 0.6">( Image credit: 3DDFA_V2 )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MindSpore-paper-code-3/code3/tree/main/FaceDetection
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
https://github.com/MindSpore-paper-code-3/code3/tree/main/FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/MindSpore-paper-code-3/code3/tree/main/FaceRecognitionForTracking
https://github.com/MindSpore-paper-code-3/code4/tree/main/HiFaceGAN
HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
https://github.com/MindSpore-paper-code-3/code5/tree/main/ReIDStrongBaseline
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MindSpore-paper-code-3/code5/tree/main/resnet50_adv_pruning
https://github.com/MindSpore-paper-code-3/code5/tree/main/retinanet_resnet101
https://github.com/MindSpore-paper-code-3/code5/tree/main/retinanet_resnet152
https://github.com/MindSpore-paper-code-3/code6/tree/main/ADNet
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
https://github.com/MindSpore-paper-code-3/code6/tree/main/cct
https://github.com/MindSpore-paper-code-3/code6/tree/main/cdp
Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition
https://github.com/MindSpore-paper-code-3/code6/tree/main/centerface
CenterFace: Joint Face Detection and Alignment Using Face as Point
https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceDetection
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceRecognitionForTracking
https://github.com/MindSpore-paper-code-3/code7/tree/main/faceboxes
https://github.com/MindSpore-paper-code-3/code8/tree/main/DeepID
https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceDetection
Face Detection with End-to-End Integration of a ConvNet and a 3D Model
https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceRecognition
https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceRecognitionForTracking
https://github.com/MindSpore-paper-code-3/code8/tree/main/faceboxes
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/MindSpore-paper-code-3/code9/tree/main/PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/MindSpore-paper-code-3/code9/tree/main/PointNet2
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/MindSpore-paper-code-3/code9/tree/main/RetinaFace_ResNet50
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/MindSpore-scientific-2/code-10/tree/main/mgan-mindspore
GANimation: Anatomically-aware Facial Animation from a Single Image
https://github.com/MindSpore-scientific-2/code-11/tree/main/SV-X-Softmax
https://github.com/MindSpore-scientific-2/code-2/tree/main/ZoneoutRNN
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
https://github.com/MindSpore-scientific-2/code-4/tree/main/mgan-mindspore
GANimation: Anatomically-aware Facial Animation from a Single Image
https://github.com/MindSpore-scientific-2/code-5/tree/main/Last-Query-Transformer-RNN
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
https://github.com/MindSpore-scientific-2/code-7/tree/main/HCP_Avatar_Inferrence
GSmoothFace: Generalized Smooth Talking Face Generation via Fine Grained 3D Face Guidance
https://github.com/MindSpore-scientific-2/code-9/tree/main/Deep_Feature_Factorization_For_Concept_Discovery
https://github.com/MindSpore-scientific/code-12/tree/main/Deep_Feature_Factorization_For_Concept_Discovery
https://github.com/MindSpore-scientific/code-2/tree/main/Three-Dimensional-Lip-Motion-Network-for-Text-Independent-Speaker-Recognition-master
Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.